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Zheng R, Bu C, Chen Y, Wei Y, Zhou B, Jiang Y, Zhu C, Wang K, Wang C, Li S, Han S, Zhang Y, Cheng J. Decreased intrinsic neural timescale in treatment-naïve adolescent depression. J Affect Disord 2024; 348:389-397. [PMID: 38160888 DOI: 10.1016/j.jad.2023.12.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/25/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in emotional and cognitive processes, whose neurobiological basis remains unclear. In this study, we applied a relatively new developed resting-state functional magnetic resonance imaging (rs-fMRI) method of intrinsic neural timescale (INT), which reflects how long neural information is stored in a local brain area and reflects an ability of information integration, to investigate the local intrinsic neural dynamics using univariate and multivariate analyses in adolescent depression. METHOD Based on the rs-fMRI data of sixty-six treatment-naïve adolescents with MDD and fifty-two well-matched healthy controls (HCs), we calculated an INT by assessing the magnitude of autocorrelation of the resting-state brain activity, and then compared the difference of INT between the two groups. Correlation between abnormal INT and clinical features was performed. We also utilized multivariate pattern analysis to determine whether INT could differentiate MDD patients from HCs at the individual level. RESULT Compared with HCs, patients with MDD showed shorter INT widely distributed in cortical and partial subcortical regions. Interestingly, the decreased INT in the left hippocampus was related to disease severity of MDD. Furthermore, INT can distinguish MDD patients from HCs with the most discriminative regions located in the dorsolateral prefrontal cortex, angular, middle occipital gyrus, and cerebellar posterior lobe. CONCLUSION Our research aids in advancing understanding the brain abnormalities of treatment-naïve adolescents with MDD from the perspective of the local neural dynamics, highlighting the significant role of INT in understanding neurophysiological mechanisms. This study shows that the altered intrinsic timescales of local neural signals widely distributed in higher-order brain cortices regions may be the neurodynamic basis of cognitive and emotional disturbances in MDD patients, and provides preliminary support for the suggestion that these could be used to aid the identification of MDD patients in clinical practice.
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Affiliation(s)
- Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Chunxiao Bu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Chendi Zhu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Henan University of Chinese Medicine, PR China
| | - Kefan Wang
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, PR China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, PR China.
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Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
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Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
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Lurie DJ, Pappas I, D'Esposito M. Cortical timescales and the modular organization of structural and functional brain networks. Hum Brain Mapp 2024; 45:e26587. [PMID: 38339903 PMCID: PMC10823764 DOI: 10.1002/hbm.26587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 12/01/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024] Open
Abstract
Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.
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Affiliation(s)
- Daniel J. Lurie
- Department of PsychologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPennsylvaniaUSA
| | - Ioannis Pappas
- Department of Neurology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mark D'Esposito
- Department of Psychology and Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
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4
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Luppi AI, Girn M, Rosas FE, Timmermann C, Roseman L, Erritzoe D, Nutt DJ, Stamatakis EA, Spreng RN, Xing L, Huttner WB, Carhart-Harris RL. A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex. Brain 2024; 147:56-80. [PMID: 37703310 DOI: 10.1093/brain/awad311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/14/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, CB2 1SB, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - Manesh Girn
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London, SW7 2AZ, UK
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David Erritzoe
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
| | - Lei Xing
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Wieland B Huttner
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Robin L Carhart-Harris
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
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5
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Brice Azangue A, Megam Ngouonkadi EB, Kabong Nono M, Fotsin HB, Sone Ekonde M, Yemele D. Stability and synchronization in neural network with delayed synaptic connections. CHAOS (WOODBURY, N.Y.) 2024; 34:013117. [PMID: 38215223 DOI: 10.1063/5.0175408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/04/2023] [Indexed: 01/14/2024]
Abstract
In this paper, we investigate the stability of the synchronous state in a complex network using the master stability function technique. We use the extended Hindmarsh-Rose neuronal model including time delayed electrical, chemical, and hybrid couplings. We find the corresponding master stability equation that describes the whole dynamics for each coupling mode. From the maximum Lyapunov exponent, we deduce the stability state for each coupling mode. We observe that for electrical coupling, there exists a mixing between stable and unstable states. For a good setting of some system parameters, the position and the size of unstable areas can be modified. For chemical coupling, we observe difficulties in having a stable area in the complex plane. For hybrid coupling, we observe a stable behavior in the whole system compared to the case where these couplings are considered separately. The obtained results for each coupling mode help to analyze the stability state of some network topologies by using the corresponding eigenvalues. We observe that using electrical coupling can involve a full or partial stability of the system. In the case of chemical coupling, unstable states are observed whereas in the case of hybrid interactions a full stability of the network is obtained. Temporal analysis of the global synchronization is also done for each coupling mode, and the results show that when the network is stable, the synchronization is globally observed, while in the case when it is unstable, its nodes are not globally synchronized.
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Affiliation(s)
- A Brice Azangue
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
| | - E B Megam Ngouonkadi
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
- Department of Electrical and Electronic Engineering, College of Technology (COT), University of Buea, P.O. Box 63 Buea, Cameroon
| | - M Kabong Nono
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
| | - H B Fotsin
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
| | - M Sone Ekonde
- Department of Electrical and Electronic Engineering, College of Technology (COT), University of Buea, P.O. Box 63 Buea, Cameroon
| | - D Yemele
- Research Unit of Mechanics and Modeling of Physical Systems, Department of Physics, Faculty of Sciences, University of Dschang, P.O. Box 067 Dschang, Cameroon
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6
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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7
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Jangraw DC, Finn ES, Bandettini PA, Landi N, Sun H, Hoeft F, Chen G, Pugh KR, Molfese PJ. Inter-subject correlation during long narratives reveals widespread neural correlates of reading ability. Neuroimage 2023; 282:120390. [PMID: 37751811 PMCID: PMC10783814 DOI: 10.1016/j.neuroimage.2023.120390] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 09/19/2023] [Accepted: 09/23/2023] [Indexed: 09/28/2023] Open
Abstract
Recent work using fMRI inter-subject correlation analysis has provided new information about the brain's response to video and audio narratives, particularly in frontal regions not typically activated by single words. This approach is very well suited to the study of reading, where narrative is central to natural experience. But since past reading paradigms have primarily presented single words or phrases, the influence of narrative on semantic processing in the brain - and how that influence might change with reading ability - remains largely unexplored. In this study, we presented coherent stories to adolescents and young adults with a wide range of reading abilities. The stories were presented in alternating visual and auditory blocks. We used a dimensional inter-subject correlation analysis to identify regions in which better and worse readers had varying levels of consistency with other readers. This analysis identified a widespread set of brain regions in which activity timecourses were more similar among better readers than among worse readers. These differences were not detected with standard block activation analyses. Worse readers had higher correlation with better readers than with other worse readers, suggesting that the worse readers had "idiosyncratic" responses rather than using a single compensatory mechanism. Close inspection confirmed that these differences were not explained by differences in IQ or motion. These results suggest an expansion of the current view of where and how reading ability is reflected in the brain, and in doing so, they establish inter-subject correlation as a sensitive tool for future studies of reading disorders.
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Affiliation(s)
- David C Jangraw
- Section on Functional Imaging Methods, NIMH, Bethesda, MD, United States; Emotion and Development Branch, NIMH, Bethesda, MD, United States; Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, United States.
| | - Emily S Finn
- Section on Functional Imaging Methods, NIMH, Bethesda, MD, United States; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Peter A Bandettini
- Section on Functional Imaging Methods, NIMH, Bethesda, MD, United States; Center for Multimodal Neuroimaging, NIMH, Bethesda, MD, United States
| | - Nicole Landi
- Haskins Laboratories, New Haven, CT, United States
| | - Haorui Sun
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, United States
| | - Fumiko Hoeft
- Haskins Laboratories, New Haven, CT, United States; Department of Psychological Sciences, University of Connecticut, Hartford, CT, United States
| | - Gang Chen
- Statistical Computing Core, NIMH, Bethesda, MD, United States
| | - Kenneth R Pugh
- Haskins Laboratories, New Haven, CT, United States; Department of Pediatrics, Yale University School of Medicine, New Haven, CT, United States
| | - Peter J Molfese
- Center for Multimodal Neuroimaging, NIMH, Bethesda, MD, United States; Haskins Laboratories, New Haven, CT, United States
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Wu X, Guo Y, Xue J, Dong Y, Sun Y, Wang B, Xiang J, Liu Y. Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs. Brain Sci 2023; 13:1331. [PMID: 37759932 PMCID: PMC10526475 DOI: 10.3390/brainsci13091331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/27/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.
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Affiliation(s)
- Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yanqing Dong
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital, Taiyuan 030013, China
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9
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Lurie DJ, Pappas I, D'Esposito M. Cortical timescales and the modular organization of structural and functional brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548751. [PMID: 37502887 PMCID: PMC10370009 DOI: 10.1101/2023.07.12.548751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.
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Affiliation(s)
- Daniel J Lurie
- Department of Psychology, University of California, Berkeley
| | - Ioannis Pappas
- Department of Neurology, Keck School of Medicine, University of Southern California
| | - Mark D'Esposito
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley
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10
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Monroe DC, Berry NT, Fino PC, Rhea CK. A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations. SENSORS (BASEL, SWITZERLAND) 2023; 23:6296. [PMID: 37514591 PMCID: PMC10385586 DOI: 10.3390/s23146296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.
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Affiliation(s)
- Derek C Monroe
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
| | - Nathaniel T Berry
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
- Under Armour, Inc., Innovation, Baltimore, MD 21230, USA
| | - Peter C Fino
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher K Rhea
- College of Health Sciences, Old Dominion University, Norfolk, VA 23508, USA
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11
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Whitfield JF, Rennie K, Chakravarthy B. Alzheimer's Disease and Its Possible Evolutionary Origin: Hypothesis. Cells 2023; 12:1618. [PMID: 37371088 DOI: 10.3390/cells12121618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/29/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The enormous, 2-3-million-year evolutionary expansion of hominin neocortices to the current enormity enabled humans to take over the planet. However, there appears to have been a glitch, and it occurred without a compensatory expansion of the entorhinal cortical (EC) gateway to the hippocampal memory-encoding system needed to manage the processing of the increasing volume of neocortical data converging on it. The resulting age-dependent connectopathic glitch was unnoticed by the early short-lived populations. It has now surfaced as Alzheimer's disease (AD) in today's long-lived populations. With advancing age, processing of the converging neocortical data by the neurons of the relatively small lateral entorhinal cortex (LEC) inflicts persistent strain and high energy costs on these cells. This may result in their hyper-release of harmless Aβ1-42 monomers into the interstitial fluid, where they seed the formation of toxic amyloid-β oligomers (AβOs) that initiate AD. At the core of connectopathic AD are the postsynaptic cellular prion protein (PrPC). Electrostatic binding of the negatively charged AβOs to the positively charged N-terminus of PrPC induces hyperphosphorylation of tau that destroys synapses. The spread of these accumulating AβOs from ground zero is supported by Aβ's own production mediated by target cells' Ca2+-sensing receptors (CaSRs). These data suggest that an early administration of a strongly positively charged, AβOs-interacting peptide or protein, plus an inhibitor of CaSR, might be an effective AD-arresting therapeutic combination.
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Affiliation(s)
- James F Whitfield
- Human Health Therapeutics, National Research Council, Ottawa, ON K1A 0R6, Canada
| | - Kerry Rennie
- Human Health Therapeutics, National Research Council, Ottawa, ON K1A 0R6, Canada
| | - Balu Chakravarthy
- Human Health Therapeutics, National Research Council, Ottawa, ON K1A 0R6, Canada
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12
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Xi C, Li A, Lai J, Huang X, Zhang P, Yan S, Jiao M, Huang H, Hu S. Brain-gut microbiota multimodal predictive model in patients with bipolar depression. J Affect Disord 2023; 323:140-152. [PMID: 36400152 DOI: 10.1016/j.jad.2022.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/28/2022] [Accepted: 11/07/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction. METHODS At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders. RESULTS The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified,Porphyromonas_asaccharolytica,Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality. LIMITATIONS Lack post-treatment and external validation datasets; size of HCs is modest. CONCLUSIONS Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.
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Affiliation(s)
- Caixi Xi
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; The Key Laboratory of Mental Disorders' Management in Zhejiang Province, Hangzhou 310003, China; Brain Research Institute of Zhejiang University, China; Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China; MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310003, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310003, China
| | - Ang Li
- Gene Hospital of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jianbo Lai
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; The Key Laboratory of Mental Disorders' Management in Zhejiang Province, Hangzhou 310003, China; Brain Research Institute of Zhejiang University, China; Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China; MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310003, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310003, China
| | - Xiaojie Huang
- Polytechnic Institute of Zhejiang University, Hangzhou 310015, China
| | - Peifen Zhang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; The Key Laboratory of Mental Disorders' Management in Zhejiang Province, Hangzhou 310003, China; Brain Research Institute of Zhejiang University, China; Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China; MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310003, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310003, China
| | - Su Yan
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Mengfan Jiao
- Gene Hospital of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Huimin Huang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Shaohua Hu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; The Key Laboratory of Mental Disorders' Management in Zhejiang Province, Hangzhou 310003, China; Brain Research Institute of Zhejiang University, China; Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China; MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310003, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310003, China.
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13
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Kringelbach ML, Perl YS, Tagliazucchi E, Deco G. Toward naturalistic neuroscience: Mechanisms underlying the flattening of brain hierarchy in movie-watching compared to rest and task. SCIENCE ADVANCES 2023; 9:eade6049. [PMID: 36638163 PMCID: PMC9839335 DOI: 10.1126/sciadv.ade6049] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/13/2022] [Indexed: 06/04/2023]
Abstract
Identifying the functional specialization of the brain has moved from using cognitive tasks and resting state to using ecological relevant, naturalistic movies. We leveraged a large-scale neuroimaging dataset to directly investigate the hierarchical reorganization of functional brain activity when watching naturalistic films compared to performing seven cognitive tasks and resting. A thermodynamics-inspired whole-brain model paradigm revealed the generative underlying mechanisms for changing the balance in causal interactions between brain regions in different conditions. Paradoxically, the hierarchy is flatter for movie-watching, and the level of nonreversibility is significantly smaller in comparison to both rest and tasks, where the latter in turn have the highest levels of hierarchy and nonreversibility. The underlying mechanisms were revealed by the model-based generative effective connectivity (GEC). Naturalistic films could therefore provide a fast and convenient way to measure important changes in GEC (integrating functional and anatomical connectivity) found in, for example, neuropsychiatric disorders. Overall, this study demonstrates the benefits of moving toward a more naturalistic neuroscience.
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Affiliation(s)
- Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
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14
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Klar P, Çatal Y, Langner R, Huang Z, Northoff G. Scale-free dynamics of core-periphery topography. Hum Brain Mapp 2022; 44:1997-2017. [PMID: 36579661 PMCID: PMC9980897 DOI: 10.1002/hbm.26187] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/15/2022] [Accepted: 12/11/2022] [Indexed: 12/30/2022] Open
Abstract
The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.
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Affiliation(s)
- Philipp Klar
- Medical Faculty, C. & O. Vogt‐Institute for Brain ResearchHeinrich Heine University of DüsseldorfDüsseldorfGermany
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Robert Langner
- Institute of Systems NeuroscienceHeinrich Heine University DusseldorfDusseldorfGermany,Institute of Neuroscience and MedicineBrain & Behaviour (INM‐7), Research Centre JülichJülichGermany
| | - Zirui Huang
- Department of AnesthesiologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA,Center for Consciousness ScienceUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada,Centre for Cognition and Brain DisordersHangzhou Normal UniversityHangzhouZhejiangChina
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15
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Pavlidis E, Campillo F, Goldbeter A, Desroches M. Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of bipolar disorder. Cogn Neurodyn 2022. [DOI: 10.1007/s11571-022-09900-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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16
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Madan Mohan V, Banerjee A. A perturbative approach to study information communication in brain networks. Netw Neurosci 2022; 6:1275-1295. [PMID: 38800461 PMCID: PMC11117119 DOI: 10.1162/netn_a_00260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 06/15/2022] [Indexed: 05/29/2024] Open
Abstract
How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is, however, restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we adapt a recently established network analysis method based on perturbations, it to a neuroscientific setting to study how information flow in the brain can raise from properties of underlying structure. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-dynamical interactions, termed net influence and flow. We also study the network-dynamical interactions at the level of resting-state networks. An attractive feature of this method is its simplicity, which allows a direct translation to an experimental or clinical setting, such as for identifying targets for stimulation studies or therapeutic interventions.
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17
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John YJ, Sawyer KS, Srinivasan K, Müller EJ, Munn BR, Shine JM. It's about time: Linking dynamical systems with human neuroimaging to understand the brain. Netw Neurosci 2022; 6:960-979. [PMID: 36875012 PMCID: PMC9976648 DOI: 10.1162/netn_a_00230] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/04/2022] [Indexed: 11/04/2022] Open
Abstract
Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.
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Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA
| | - Kayle S. Sawyer
- Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Boston VA Healthcare System, Boston, MA, USA
- Sawyer Scientific, LLC, Boston, MA, USA
| | - Karthik Srinivasan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eli J. Müller
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - Brandon R. Munn
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
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18
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Martinez-Saito M. Discrete scaling and criticality in a chain of adaptive excitable integrators. CHAOS, SOLITONS & FRACTALS 2022; 163:112574. [DOI: 10.1016/j.chaos.2022.112574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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19
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Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics. Neuroimage Clin 2022; 35:103108. [PMID: 35917719 PMCID: PMC9421449 DOI: 10.1016/j.nicl.2022.103108] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/01/2022] [Accepted: 07/10/2022] [Indexed: 11/16/2022]
Abstract
Multiple sclerosis (MS) can be considered as a network disorder. This review discusses network concepts in order to understand progression in MS. Damage is hypothesized to lead to a “network collapse” and clinical progression. New concepts are discussed that will likely influence the field in the near future. These include brain wiring, how regions communicate and robustness to damage.
Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a “network collapse”. After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.
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Affiliation(s)
- Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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20
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Replay, the default mode network and the cascaded memory systems model. Nat Rev Neurosci 2022; 23:628-640. [PMID: 35970912 DOI: 10.1038/s41583-022-00620-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 12/25/2022]
Abstract
The spontaneous replay of patterns of activity related to past experiences and memories is a striking feature of brain activity, as is the coherent activation of sets of brain areas - particularly those comprising the default mode network (DMN) - during rest. We propose that these two phenomena are strongly intertwined and that their potential functions overlap. In the 'cascaded memory systems model' that we outline here, we hypothesize that the DMN forms the backbone for the propagation of replay, mediating interactions between the hippocampus and the neocortex that enable the consolidation of new memories. The DMN may also independently ignite replay cascades, which support reactivation of older memories or high-level semantic representations. We suggest that transient cortical activations, inducing long-range correlations across the neocortex, are a key mechanism supporting a hierarchy of representations that progresses from simple percepts to semantic representations of causes and, finally, to whole episodes.
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21
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Buendía V, Villegas P, Burioni R, Muñoz MA. The broad edge of synchronization: Griffiths effects and collective phenomena in brain networks. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20200424. [PMID: 35599563 DOI: 10.1098/rsta.2020.0424] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Many of the amazing functional capabilities of the brain are collective properties stemming from the interactions of large sets of individual neurons. In particular, the most salient collective phenomena in brain activity are oscillations, which require the synchronous activation of many neurons. Here, we analyse parsimonious dynamical models of neural synchronization running on top of synthetic networks that capture essential aspects of the actual brain anatomical connectivity such as a hierarchical-modular and core-periphery structure. These models reveal the emergence of complex collective states with intermediate and flexible levels of synchronization, halfway in the synchronous-asynchronous spectrum. These states are best described as broad Griffiths-like phases, i.e. an extension of standard critical points that emerge in structurally heterogeneous systems. We analyse different routes (bifurcations) to synchronization and stress the relevance of 'hybrid-type transitions' to generate rich dynamical patterns. Overall, our results illustrate the complex interplay between structure and dynamics, underlining key aspects leading to rich collective states needed to sustain brain functionality. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Victor Buendía
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Pablo Villegas
- IMT Institute for Advanced Studies, Piazza San Ponziano 6 55100 Lucca, Italy
| | - Raffaella Burioni
- Dipartimento di Matematica, Fisica e Informatica, Università di Parma, via G.P. Usberti, 7/A - 43124, Parma, Italy
- INFN, Gruppo Collegato di Parma, via G.P. Usberti, 7/A - 43124, Parma, Italy
| | - Miguel A Muñoz
- Departamento de Electromagnetismo y Física de la Materia e Instituto Carlos I de Física Teórica y Computacional. Universidad de Granada, E-18071 Granada, Spain
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22
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Petkoski S, Jirsa VK. Normalizing the brain connectome for communication through synchronization. Netw Neurosci 2022; 6:722-744. [PMID: 36607179 PMCID: PMC9810372 DOI: 10.1162/netn_a_00231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/10/2022] [Indexed: 01/10/2023] Open
Abstract
Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual, particle-wave, perspective, integrate time delays due to finite transmission speeds, and derive a normalization of the connectome. When applied to the database of the Human Connectome Project, it explains the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N = 100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel, here-proposed, generalized framework of structural connectivity.
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Affiliation(s)
- Spase Petkoski
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
| | - Viktor K. Jirsa
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
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23
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Northoff G, Fraser M, Griffiths J, Pinotsis DA, Panangaden P, Moran R, Friston K. Augmenting Human Selves Through Artificial Agents – Lessons From the Brain. Front Comput Neurosci 2022; 16:892354. [PMID: 35814345 PMCID: PMC9260143 DOI: 10.3389/fncom.2022.892354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/13/2022] [Indexed: 01/04/2023] Open
Abstract
Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.
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Affiliation(s)
- Georg Northoff
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- Department of Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
- Centre for Research Ethics & Bioethics, Uppsala University, Uppsala, Sweden
| | - Maia Fraser
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada
- *Correspondence: Maia Fraser,
| | - John Griffiths
- Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Dimitris A. Pinotsis
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Prakash Panangaden
- Department of Computer Science, McGill University, Montreal, QC, Canada
- Montreal Institute for Learning Algorithms (MILA)., Montreal, QC, Canada
| | - Rosalyn Moran
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, London, United Kingdom
- Institute of Neurology, University College London, London, United Kingdom
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24
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Liu Q, Shi Z, Wang K, Liu T, Funahashi S, Wu J, Zhang J. Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson’s Patients. Front Comput Neurosci 2022; 16:891384. [PMID: 35720771 PMCID: PMC9204483 DOI: 10.3389/fncom.2022.891384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Previous studies have demonstrated a close relationship between early Parkinson’s disease and functional network abnormalities. However, the pattern of brain changes in the early stages of Parkinson’s disease has not been confirmed, which has important implications for the study of clinical indicators of Parkinson’s disease. Therefore, we investigated the functional connectivity before and after treatment in patients with early Parkinson’s disease, and further investigated the relationship between some topological properties and clinicopathological indicators. We included resting state-fMRI (rs-fMRI) data from 27 patients with early Parkinson’s disease aged 50–75 years from the Parkinson’s Disease Progression Markers Initiative (PPMI). The results showed that the functional connectivity of 6 networks, cerebellum network (CBN), cingulo_opercular network (CON), default network (DMN), fronto-parietal network (FPN), occipital network (OCC), and sensorimotor network (SMN), was significantly changed. Compared to before treatment, the main functional connections were concentrated in the CBN after treatment. In addition, the coefficients of these nodes have also changed. For betweenness centrality (BC), the FPN showed a significant improvement in treatment (p < 0.001). In conclusion, the alteration of functional networks in early Parkinson’s patients is critical for clarifying the mechanisms of early diagnosis of the disease.
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Affiliation(s)
- Qing Liu
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - ZhongYan Shi
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Kexin Wang
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Jinglong Wu,
| | - Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
- Jian Zhang,
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25
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Northoff G, Vatansever D, Scalabrini A, Stamatakis EA. Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models. Neuroscientist 2022:10738584221081752. [PMID: 35611670 DOI: 10.1177/10738584221081752] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.
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26
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Kim M, Harris RE, DaSilva AF, Lee U. Explosive Synchronization-Based Brain Modulation Reduces Hypersensitivity in the Brain Network: A Computational Model Study. Front Comput Neurosci 2022; 16:815099. [PMID: 35311218 PMCID: PMC8927545 DOI: 10.3389/fncom.2022.815099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/18/2022] [Indexed: 11/29/2022] Open
Abstract
Fibromyalgia (FM) is a chronic pain condition that is characterized by hypersensitivity to multimodal sensory stimuli, widespread pain, and fatigue. We have previously proposed explosive synchronization (ES), a phenomenon wherein a small perturbation to a network can lead to an abrupt state transition, as a potential mechanism of the hypersensitive FM brain. Therefore, we hypothesized that converting a brain network from ES to general synchronization (GS) may reduce the hypersensitivity of FM brain. To find an effective brain network modulation to convert ES into GS, we constructed a large-scale brain network model near criticality (i.e., an optimally balanced state between order and disorders), which reflects brain dynamics in conscious wakefulness, and adjusted two parameters: local structural connectivity and signal randomness of target brain regions. The network sensitivity to global stimuli was compared between the brain networks before and after the modulation. We found that only increasing the local connectivity of hubs (nodes with intense connections) changes ES to GS, reducing the sensitivity, whereas other types of modulation such as decreasing local connectivity, increasing and decreasing signal randomness are not effective. This study would help to develop a network mechanism-based brain modulation method to reduce the hypersensitivity in FM.
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Affiliation(s)
- MinKyung Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Richard E. Harris
- Chronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Alexandre F. DaSilva
- Headache & Orofacial Pain Effort Laboratory, Biologic & Materials Sciences Department, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
- *Correspondence: UnCheol Lee,
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27
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Golesorkhi M, Gomez-Pilar J, Çatal Y, Tumati S, Yagoub MCE, Stamatakis EA, Northoff G. From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity. Cereb Cortex 2022; 32:5637-5653. [PMID: 35188968 PMCID: PMC9753094 DOI: 10.1093/cercor/bhac042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 01/25/2023] Open
Abstract
The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.
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Affiliation(s)
| | | | - Yasir Çatal
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Shankar Tumati
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Mustapha C E Yagoub
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Emanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge CB1 0SP, United Kingdom
| | - Georg Northoff
- Corresponding author: Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada.
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28
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Northoff G, Zilio F. Temporo-spatial Theory of Consciousness (TTC) - Bridging the gap of neuronal activity and phenomenal states. Behav Brain Res 2022; 424:113788. [PMID: 35149122 DOI: 10.1016/j.bbr.2022.113788] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 01/22/2023]
Abstract
Consciousness and its neural mechanisms remain a mystery. Current neuroscientific theories focus predominantly on the external input/stimulus and the associated stimulus-related activity during conscious contents. Despite all progress, we encounter two gaps: (i) a gap between spontaneous and stimulus-related activity; (ii) a gap between neuronal and phenomenal features. A novel, different, and unique approach, Temporo-spatial theory of consciousness (TTC) aims to bridge both gaps. The TTC focuses on the brain's spontaneous activity and how its spatial topography and temporal dynamic shape stimulus-related activity and resurface in the corresponding spatial and temporal features of consciousness, i.e., 'common currency'. The TTC introduces four temporo-spatial mechanisms: expansion, globalization, alignment, and nestedness. These are associated with distinct dimensions of consciousness including phenomenal content, access, form/structure, and level/state, respectively. Following up on the first introduction of the TTC in 2017, we review updates, further develop these temporo-spatial mechanisms, and postulate specific neurophenomenal hypotheses. We conclude that the TTC offers a viable approach for (i) linking spontaneous and stimulus-related activity in conscious states; (ii) determining specific neuronal and neurophenomenal mechanisms for the distinct dimensions of consciousness; (iii) an integrative and unifying framework of different neuroscientific theories of consciousness; and (iv) offers novel empirically grounded conceptual assumptions about the biological and ontological nature of consciousness and its relation to the brain.
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Affiliation(s)
- Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| | - Federico Zilio
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padua, Italy.
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29
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Guo T, Xuan M, Zhou C, Wu J, Gao T, Bai X, Liu X, Gu L, Liu R, Song Z, Gu Q, Huang P, Pu J, Zhang B, Xu X, Guan X, Zhang M. Normalization effect of levodopa on hierarchical brain function in Parkinson’s disease. Netw Neurosci 2022; 6:552-569. [PMID: 35733432 PMCID: PMC9208001 DOI: 10.1162/netn_a_00232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/10/2022] [Indexed: 11/08/2022] Open
Abstract
Hierarchical brain organization, in which the rich club and diverse club situate in core position, is critical for global information integration in the human brain network. Parkinson’s disease (PD), a common movement disorder, has been conceptualized as a network disorder. Levodopa is an effective treatment for PD. Whether there is a functional divergence in the hierarchical brain system under PD pathology, and how this divergence is regulated by immediate levodopa therapy, remains unknown. We constructed a functional network in 61 PD patients and 89 normal controls and applied graph theoretical analyses to examine the neural mechanism of levodopa short response from the perspective of brain hierarchical configuration. The results revealed the following: (a) PD patients exhibited disrupted function within rich-club organization, while the diverse club preserved function, indicating a differentiated brain topological organization in PD. (b) Along the rich-club derivate hierarchical system, PD patients showed impaired network properties within rich-club and feeder subnetworks, and decreased nodal degree centrality in rich-club and feeder nodes, along with increased nodal degree in peripheral nodes, suggesting distinct functional patterns in different types of nodes. And (c) levodopa could normalize the abnormal network architecture of the rich-club system. This study provides evidence for levodopa effects on the hierarchical brain system with divergent functions. Many studies of brain networks have revealed densely connected regions forming the rich club and diverse club, which occupy the central position of the hierarchical brain system. Here, we explore the hierarchical topology in Parkinson’s disease (PD) and investigate the neural effect of levodopa on it. We show that within the core position of the hierarchical system, the function of the diverse club is preserved while the function of the rich club is impaired. Along the rich-club hierarchical system, the function of biologically costly rich-club and feeder subnetworks is disrupted, together with an increased function of peripheral nodes, which could be normalized by levodopa. Our study provides evidence of a disparity pattern between different levels of brain hierarchical systems under PD pathology.
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Affiliation(s)
- Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiqi Liu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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30
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Wolff A, Berberian N, Golesorkhi M, Gomez-Pilar J, Zilio F, Northoff G. Intrinsic neural timescales: temporal integration and segregation. Trends Cogn Sci 2022; 26:159-173. [PMID: 34991988 DOI: 10.1016/j.tics.2021.11.007] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 12/11/2022]
Abstract
We are continuously bombarded by external inputs of various timescales from the environment. How does the brain process this multitude of timescales? Recent resting state studies show a hierarchy of intrinsic neural timescales (INT) with a shorter duration in unimodal regions (e.g., visual cortex and auditory cortex) and a longer duration in transmodal regions (e.g., default mode network). This unimodal-transmodal hierarchy is present across acquisition modalities [electroencephalogram (EEG)/magnetoencephalogram (MEG) and fMRI] and can be found in different species and during a variety of different task states. Together, this suggests that the hierarchy of INT is central to the temporal integration (combining successive stimuli) and segregation (separating successive stimuli) of external inputs from the environment, leading to temporal segmentation and prediction in perception and cognition.
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Affiliation(s)
- Annemarie Wolff
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Nareg Berberian
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Mehrshad Golesorkhi
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicia, (CIBER-BBN), Madrid, Spain
| | - Federico Zilio
- Department of Philosophy, Sociology, Education, and Applied Psychology, University of Padova, Padua, Italy
| | - Georg Northoff
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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31
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Vuksanović V, Staff RT, Morson S, Ahearn T, Bracoud L, Murray AD, Bentham P, Kipps CM, Harrington CR, Wischik CM. Degeneration of basal and limbic networks is a core feature of behavioural variant frontotemporal dementia. Brain Commun 2021; 3:fcab241. [PMID: 34939031 PMCID: PMC8688778 DOI: 10.1093/braincomms/fcab241] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 05/13/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
The behavioural variant of frontotemporal dementia is a clinical syndrome characterized by changes in behaviour, cognition and functional ability. Although atrophy in frontal and temporal regions would appear to be a defining feature, neuroimaging studies have identified volumetric differences distributed across large parts of the cortex, giving rise to a classification into distinct neuroanatomical subtypes. Here, we extended these neuroimaging studies to examine how distributed patterns of cortical atrophy map onto brain network hubs. We used baseline structural magnetic resonance imaging data collected from 213 behavioural variant of frontotemporal dementia patients meeting consensus diagnostic criteria and having definite evidence of frontal and/or temporal lobe atrophy from a global clinical trial conducted in 70 sites in Canada, United States of America, Australia, Asia and Europe. These were compared with data from 244 healthy elderly subjects from a well-characterized cohort study. We have used statistical methods of hierarchical agglomerative clustering of 68 regional cortical and subcortical volumes (34 in each hemisphere) to determine the reproducibility of previously described neuroanatomical subtypes in a global study. We have also attempted to link the structural findings to clinical features defined systematically using well-validated clinical scales (Addenbrooke’s Cognitive Examination Revised, the Mini-Mental Status Examination, the Frontotemporal Dementia Rating Scale and the Functional Assessment Questionnaire) and subscales derived from them. Whilst we can confirm that the subtypes are robust, they have limited value in explaining the clinical heterogeneity of the syndrome. We have found that a common pattern of degeneration affecting a small number of subcortical, limbic and frontal nodes within highly connected networks (most previously identified as rich club members or functional binding nodes) is shared by all the anatomical subtypes. Degeneration in these core regions is correlated with cognitive and functional impairment, but less so with behavioural impairment. These findings suggest that degeneration in highly connected basal, limbic and frontal networks is a core feature of the behavioural variant of frontotemporal dementia phenotype irrespective of neuroanatomical and clinical heterogeneity, and may underly the impairment of integration in cognition, function and behaviour responsible for the loss of insight that characterizes the syndrome.
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Affiliation(s)
- Vesna Vuksanović
- Swansea University Medical School, Health Data Research UK, Swansea University, Swansea SA2 8PP, UK.,School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK.,TauRx Therapeutics, Aberdeen AB24 5RP, UK
| | - Roger T Staff
- Medical Physics, NHS Grampian, Aberdeen AB25 2ZD, UK
| | - Suzannah Morson
- TauRx Therapeutics, Aberdeen AB24 5RP, UK.,School of Psychology, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Trevor Ahearn
- Medical Physics, NHS Grampian, Aberdeen AB25 2ZD, UK
| | | | - Alison D Murray
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | | | - Christopher M Kipps
- University Hospital Southampton and University of Southampton, Southampton SO16 6YD, UK
| | - Charles R Harrington
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK.,TauRx Therapeutics, Aberdeen AB24 5RP, UK
| | - Claude M Wischik
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK.,TauRx Therapeutics, Aberdeen AB24 5RP, UK
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32
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Yuan Y, Pan X, Wang R. Biophysical mechanism of the interaction between default mode network and working memory network. Cogn Neurodyn 2021; 15:1101-1124. [PMID: 34786031 PMCID: PMC8572310 DOI: 10.1007/s11571-021-09674-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/11/2021] [Accepted: 03/26/2021] [Indexed: 12/16/2022] Open
Abstract
Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09674-1.
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Affiliation(s)
- Yue Yuan
- East China University of Science and Technology, Shanghai, 200237 China
| | - Xiaochuan Pan
- East China University of Science and Technology, Shanghai, 200237 China
| | - Rubin Wang
- East China University of Science and Technology, Shanghai, 200237 China
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33
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Painter DT, Daniels BC, Laubichler MD. Innovations are disproportionately likely in the periphery of a scientific network. Theory Biosci 2021; 140:391-399. [PMID: 34773205 DOI: 10.1007/s12064-021-00359-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 10/31/2021] [Indexed: 11/26/2022]
Abstract
The origins of innovation in science are typically understood using historical narratives that tend to be focused on small sets of influential authors, an approach that is rigorous but limited in scope. Here, we develop a framework for rigorously identifying innovation across an entire scientific field through automated analysis of a corpus of over 6000 documents that includes every paper published in the field of evolutionary medicine. This comprehensive approach allows us to explore statistical properties of innovation, asking where innovative ideas tend to originate within a field's pre-existing conceptual framework. First, we develop a measure of innovation based on novelty and persistence, quantifying the collective acceptance of novel language and ideas. Second, we study the field's conceptual landscape through a bibliographic coupling network. We find that innovations are disproportionately more likely in the periphery of the bibliographic coupling network, suggesting that the relative freedom allowed by remaining unconnected with well-established lines of research could be beneficial to creating novel and lasting change. In this way, the emergence of collective computation in scientific disciplines may have robustness-adaptability trade-offs that are similar to those found in other biosocial complex systems.
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Affiliation(s)
- Deryc T Painter
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA
| | - Bryan C Daniels
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA
| | - Manfred D Laubichler
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA.
- Max Planck Institute for the History of Science, Berlin, Germany.
- Santa Fe Institute, Santa Fe, New Mexico, USA.
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34
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Neudorf J, Kress S, Borowsky R. Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity. Brain Struct Funct 2021; 227:331-343. [PMID: 34633514 PMCID: PMC8741721 DOI: 10.1007/s00429-021-02403-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023]
Abstract
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).
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Affiliation(s)
- Josh Neudorf
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Shaylyn Kress
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ron Borowsky
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada.
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35
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Todd J, Yeark MD, Paton B, Jermyn A, Winkler I. Shorter Contextual Timescale Rather Than Memory Deficit in Aging. Cereb Cortex 2021; 32:2412-2423. [PMID: 34564713 DOI: 10.1093/cercor/bhab344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 11/14/2022] Open
Abstract
Many aspects of cognitive ability and brain function that change as we age look like deficits on account of measurable differences in comparison to younger adult groups. One such difference occurs in auditory sensory responses that index perceptual learning. Meta-analytic findings show reliable age-related differences in auditory responses to repetitive patterns of sound and to rare violations of those patterns, variously attributed to deficits in auditory sensory memory and inhibition. Here, we determine whether proposed deficits would render older adults less prone to primacy effects, robustly observed in young adults, which present as a tendency for first learning to have a disproportionate influence over later perceptual inference. The results confirm this reduced sensitivity to primacy effects but do not support impairment in auditory sensory memory as the origin of this difference. Instead, the aging brain produces data consistent with shorter timescales of contextual reference. In conclusion, age-related differences observed previously for perceptual inference appear highly context-specific necessitating reconsideration of whether and to what function the notion of deficit should be attributed, and even whether the notion of deficit is appropriate at all.
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Affiliation(s)
- Juanita Todd
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - Mattsen D Yeark
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - Bryan Paton
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - Alexandra Jermyn
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - István Winkler
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest H-1117, Hungary
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36
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A Pilot Trial Examining the Merits of Combining Amantadine and Repetitive Transcranial Magnetic Stimulation as an Intervention for Persons With Disordered Consciousness After TBI. J Head Trauma Rehabil 2021; 35:371-387. [PMID: 33165151 DOI: 10.1097/htr.0000000000000634] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Report pilot findings of neurobehavioral gains and network changes observed in persons with disordered consciousness (DoC) who received repetitive transcranial magnetic stimulation (rTMS) or amantadine (AMA), and then rTMS+AMA. PARTICIPANTS Four persons with DoC 1 to 15 years after traumatic brain injury (TBI). DESIGN Alternate treatment-order, within-subject, baseline-controlled trial. MAIN MEASURES For group and individual neurobehavioral analyses, predetermined thresholds, based on mixed linear-effects models and conditional minimally detectable change, were used to define meaningful neurobehavioral change for the Disorders of Consciousness Scale-25 (DOCS) total and Auditory-Language measures. Resting-state functional connectivity (rsFC) of the default mode and 6 other networks was examined. RESULTS Meaningful gains in DOCS total measures were observed for 75% of treatment segments and auditory-language gains were observed after rTMS, which doubled when rTMS preceded rTMS+AMA. Neurobehavioral changes were reflected in rsFC for language, salience, and sensorimotor networks. Between networks interactions were modulated, globally, after all treatments. CONCLUSIONS For persons with DoC 1 to 15 years after TBI, meaningful neurobehavioral gains were observed after provision of rTMS, AMA, and rTMS+AMA. Sequencing and combining of treatments to modulate broad-scale neural activity, via differing mechanisms, merits investigation in a future study powered to determine efficacy of this approach to enabling neurobehavioral recovery.
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37
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López-González A, Panda R, Ponce-Alvarez A, Zamora-López G, Escrichs A, Martial C, Thibaut A, Gosseries O, Kringelbach ML, Annen J, Laureys S, Deco G. Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Commun Biol 2021; 4:1037. [PMID: 34489535 PMCID: PMC8421429 DOI: 10.1038/s42003-021-02537-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 08/11/2021] [Indexed: 01/07/2023] Open
Abstract
Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states.
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Affiliation(s)
- Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Rajanikant Panda
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Charlotte Martial
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Jitka Annen
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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38
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Golesorkhi M, Gomez-Pilar J, Zilio F, Berberian N, Wolff A, Yagoub MCE, Northoff G. The brain and its time: intrinsic neural timescales are key for input processing. Commun Biol 2021; 4:970. [PMID: 34400800 PMCID: PMC8368044 DOI: 10.1038/s42003-021-02483-6] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 07/19/2021] [Indexed: 02/07/2023] Open
Abstract
We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.
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Affiliation(s)
- Mehrshad Golesorkhi
- grid.28046.380000 0001 2182 2255School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada ,grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- grid.5239.d0000 0001 2286 5329Biomedical Engineering Group, University of Valladolid, Valladolid, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Federico Zilio
- grid.5608.b0000 0004 1757 3470Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, Padua, Italy
| | - Nareg Berberian
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Annemarie Wolff
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Mustapha C. E. Yagoub
- grid.28046.380000 0001 2182 2255School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China ,grid.13402.340000 0004 1759 700XMental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
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39
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Liu T, Yan Y, Ai J, Chen D, Wu J, Fang B, Yan T. Disrupted rich-club organization of brain structural networks in Parkinson's disease. Brain Struct Funct 2021; 226:2205-2217. [PMID: 34173868 DOI: 10.1007/s00429-021-02319-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 06/08/2021] [Indexed: 12/16/2022]
Abstract
Parkinson's disease (PD) can be considered as the dysfunction in segregation and integration of large-scale structural networks in the late stage of disease progression. However, the altered patterns in the early stage have not been extensively investigated, especially the altered structural rich-club patterns, which is proved powerful to detect the altered patterns of structural networks in Alzheimer's disease and schizophrenia. To this end, we investigated the rich-club organization of the structural networks derived from diffusion tensor imaging (DTI) data in the early stage of PD, and further investigated the relationship between rich-club organization and clinicopathological measures, including motor and non-motor scales and cerebrospinal fluid (CSF) biomarkers. Two datasets were included for validation in this study. The first one included 41 healthy controls (HC) and 64 PD patients from Parkinson's Disease Progression Marker Initiative (PPMI) dataset, and the second one included 24 HC and 26 PD patients. Results revealed that PD patients in early stage had disrupted rich-club organization, with abnormal connectivity strength between peripheral regions (two-sample t-test between PD and HC: p < 0.001), whereas connectivity strength between rich-club regions remained relatively stable (two-sample t-test between PD and HC: p = 0.108). The classification accuracies on three types of connections were 59.93%, 73.96% and 77.44% for rich-club, feeder and local connections. Furthermore, abnormal local and feeder connections showed significant correlation with poor clinical scales and CSF biomarkers. In summary, a selective disruption of non-rich-club connections here could be regarded as a potential marker in the early diagnosis of PD.
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Affiliation(s)
- Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yan Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jing Ai
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan.,Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Boyan Fang
- Beijing Rehabilitation Hospital Capital Medical University, Beijing, China.
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China.
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40
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Roy D, Uddin LQ. Atypical core-periphery brain dynamics in autism. Netw Neurosci 2021; 5:295-321. [PMID: 34189366 PMCID: PMC8233106 DOI: 10.1162/netn_a_00181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/31/2020] [Indexed: 11/06/2022] Open
Abstract
The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.
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Affiliation(s)
- Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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41
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Yin D, Kaiser M. Understanding neural flexibility from a multifaceted definition. Neuroimage 2021; 235:118027. [PMID: 33836274 DOI: 10.1016/j.neuroimage.2021.118027] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/19/2021] [Accepted: 03/27/2021] [Indexed: 11/19/2022] Open
Abstract
Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.
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Affiliation(s)
- Dazhi Yin
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK; School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK; Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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42
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Golesorkhi M, Gomez-Pilar J, Tumati S, Fraser M, Northoff G. Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization. Commun Biol 2021; 4:277. [PMID: 33664456 PMCID: PMC7933253 DOI: 10.1038/s42003-021-01785-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/03/2021] [Indexed: 01/04/2023] Open
Abstract
The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography, namely the core-periphery organization, remains an open issue. Using magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region's time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex's temporal hierarchy converges with its spatial core-periphery hierarchy.
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Affiliation(s)
- Mehrshad Golesorkhi
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Shankar Tumati
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
- Neuropsychopharmacology research group, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Maia Fraser
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada.
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China.
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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43
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Daniels BC, Romanczuk P. Quantifying the impact of network structure on speed and accuracy in collective decision-making. Theory Biosci 2021; 140:379-390. [PMID: 33635501 DOI: 10.1007/s12064-020-00335-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 12/24/2020] [Indexed: 11/30/2022]
Abstract
Found in varied contexts from neurons to ants to fish, binary decision-making is one of the simplest forms of collective computation. In this process, information collected by individuals about an uncertain environment is accumulated to guide behavior at the aggregate scale. We study binary decision-making dynamics in networks responding to inputs with small signal-to-noise ratios, looking for quantitative measures of collectivity that control performance in this task. We find that decision accuracy is directly correlated with the speed of collective dynamics, which is in turn controlled by three factors: the leading eigenvalue of the network adjacency matrix, the corresponding eigenvector's participation ratio, and distance from the corresponding symmetry-breaking bifurcation. A novel approximation of the maximal attainable timescale near such a bifurcation allows us to predict how decision-making performance scales in large networks based solely on their spectral properties. Specifically, we explore the effects of localization caused by the hierarchical assortative structure of a "rich club" topology. This gives insight into the trade-offs involved in the higher-order structure found in living networks performing collective computations.
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Affiliation(s)
- Bryan C Daniels
- ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ, USA.
| | - Pawel Romanczuk
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
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44
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Brain network motifs are markers of loss and recovery of consciousness. Sci Rep 2021; 11:3892. [PMID: 33594110 PMCID: PMC7887248 DOI: 10.1038/s41598-021-83482-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 02/03/2021] [Indexed: 01/12/2023] Open
Abstract
Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8-13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall's W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.
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45
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Stochastic synchronization of dynamics on the human connectome. Neuroimage 2021; 229:117738. [PMID: 33454400 DOI: 10.1016/j.neuroimage.2021.117738] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 11/30/2020] [Accepted: 01/09/2021] [Indexed: 11/20/2022] Open
Abstract
Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome's subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.
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46
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Huo S, Tian C, Zheng M, Guan S, Zhou C, Liu Z. Spatial multi-scaled chimera states of cerebral cortex network and its inherent structure-dynamics relationship in human brain. Natl Sci Rev 2021; 8:nwaa125. [PMID: 34691552 PMCID: PMC8288421 DOI: 10.1093/nsr/nwaa125] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 04/01/2020] [Accepted: 05/21/2020] [Indexed: 11/20/2022] Open
Abstract
Human cerebral cortex displays various dynamics patterns under different states, however the mechanism how such diverse patterns can be supported by the underlying brain network is still not well understood. Human brain has a unique network structure with different regions of interesting to perform cognitive tasks. Using coupled neural mass oscillators on human cortical network and paying attention to both global and local regions, we observe a new feature of chimera states with multiple spatial scales and a positive correlation between the synchronization preference of local region and the degree of symmetry of the connectivity of the region in the network. Further, we use the concept of effective symmetry in the network to build structural and dynamical hierarchical trees and find close matching between them. These results help to explain the multiple brain rhythms observed in experiments and suggest a generic principle for complex brain network as a structure substrate to support diverse functional patterns.
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Affiliation(s)
- Siyu Huo
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
| | - Changhai Tian
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
- School of Data Science, Tongren University, Tongren 554300, China
| | - Muhua Zheng
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
| | - Shuguang Guan
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, China
- Department of Physics, Zhejiang University, Hangzhou 310058, China
| | - Zonghua Liu
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
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47
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Shafiei G, Markello RD, Vos de Wael R, Bernhardt BC, Fulcher BD, Misic B. Topographic gradients of intrinsic dynamics across neocortex. eLife 2020; 9:e62116. [PMID: 33331819 PMCID: PMC7771969 DOI: 10.7554/elife.62116] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/16/2020] [Indexed: 12/16/2022] Open
Abstract
The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Ben D Fulcher
- School of Physics, The University of SydneySydneyAustralia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
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48
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Kolvoort IR, Wainio‐Theberge S, Wolff A, Northoff G. Temporal integration as "common currency" of brain and self-scale-free activity in resting-state EEG correlates with temporal delay effects on self-relatedness. Hum Brain Mapp 2020; 41:4355-4374. [PMID: 32697351 PMCID: PMC7502844 DOI: 10.1002/hbm.25129] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/01/2020] [Accepted: 06/24/2020] [Indexed: 01/05/2023] Open
Abstract
The self is a multifaceted phenomenon that integrates information and experience across multiple time scales. How temporal integration on the psychological level of the self is related to temporal integration on the neuronal level remains unclear. To investigate temporal integration on the psychological level, we modified a well-established self-matching paradigm by inserting temporal delays. On the neuronal level, we indexed temporal integration in resting-state EEG by two related measures of scale-free dynamics, the power law exponent and autocorrelation window. We hypothesized that the previously established self-prioritization effect, measured as decreased response times or increased accuracy for self-related stimuli, would change with the insertion of different temporal delays between the paired stimuli, and that these changes would be related to temporal integration on the neuronal level. We found a significant self-prioritization effect on accuracy in all conditions with delays, indicating stronger temporal integration of self-related stimuli. Further, we observed a relationship between temporal integration on psychological and neuronal levels: higher degrees of neuronal integration, that is, higher power-law exponent and longer autocorrelation window, during resting-state EEG were related to a stronger increase in the self-prioritization effect across longer temporal delays. We conclude that temporal integration on the neuronal level serves as a template for temporal integration of the self on the psychological level. Temporal integration can thus be conceived as the "common currency" of neuronal and psychological levels of self.
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Affiliation(s)
- Ivar R. Kolvoort
- Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health ResearchUniversity of OttawaOttawaOntarioCanada
- Department of Psychology, Programme Group Psychological MethodsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Soren Wainio‐Theberge
- Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health ResearchUniversity of OttawaOttawaOntarioCanada
| | - Annemarie Wolff
- Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health ResearchUniversity of OttawaOttawaOntarioCanada
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health ResearchUniversity of OttawaOttawaOntarioCanada
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49
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Papadopoulos L, Lynn CW, Battaglia D, Bassett DS. Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state. PLoS Comput Biol 2020; 16:e1008144. [PMID: 32886673 PMCID: PMC7537889 DOI: 10.1371/journal.pcbi.1008144] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 10/06/2020] [Accepted: 07/12/2020] [Indexed: 01/09/2023] Open
Abstract
At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity—in concert with network structure—affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions’ baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations. Stimulation can be used to alter brain activity and is a therapeutic option for certain neurological conditions. However, predicting the distributed effects of local perturbations is difficult. Previous studies show that responses to stimulation depend on anatomical (or structural) coupling. In addition to structure, here we consider how stimulation effects also depend on the brain’s collective dynamical (or functional) state, arising from the coordination of rhythmic activity across large-scale networks. In a whole-brain computational model, we show that global responses to regional stimulation can indeed be contingent upon and differ across various dynamical working points. Notably, depending on the network’s oscillatory regime, stimulation can accelerate the activity of the stimulated site, and lead to widespread effects at both the new, excited frequency, as well as in a much broader frequency range including areas’ baseline frequencies. While structural connectivity is a good predictor of “excited band” changes, in some states “baseline band” effects can be better predicted by functional connectivity, which depends upon the system’s oscillatory regime. By integrating and extending past efforts, our results thus indicate that dynamical—in additional to structural—brain organization plays a role in governing how focal stimulation modulates interactions between distributed network elements.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Christopher W. Lynn
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France
| | - Danielle S. Bassett
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
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50
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Fallon J, Ward PGD, Parkes L, Oldham S, Arnatkevičiūtė A, Fornito A, Fulcher BD. Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain. Netw Neurosci 2020; 4:788-806. [PMID: 33615091 PMCID: PMC7888482 DOI: 10.1162/netn_a_00151] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 06/08/2020] [Indexed: 12/21/2022] Open
Abstract
Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain's underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold.
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Affiliation(s)
- John Fallon
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Phillip G. D. Ward
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Australia
| | - Linden Parkes
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Ben D. Fulcher
- Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Australia
- School of Physics, University of Sydney, NSW, Australia
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